# Phase 4: Synthetic Control Method — Interpretation Notes

## Summary Table

| Country | Treat yr | Avg gap (pp) | RMSPE ratio | p-value | Pre-fit | Interpretation |
|---------|----------|-------------|-------------|---------|---------|----------------|
| MNG     | 1996     | -9.4        | 5.63        | 0.102   | Good (2.9) | CA worsened substantially; marginally significant |
| RUS     | 2000     | +9.1        | 0.59        | 0.983   | Poor (17.9) | Bad pre-fit; gap SMALLER post-opening; uninformative |
| AZE     | 2002     | +23.3       | 2.92        | 0.170   | Moderate (10.2) | Massive oil boom dominates; not demographic |
| BLR     | 2007     | -3.1        | 2.13        | 0.177   | Good (2.8) | Modest CA deterioration; borderline |
| TJK     | 2008     | +2.2        | 0.48        | 1.000   | Poor (16.6) | Bad pre-fit; completely uninformative |
| GEO     | 2012     | -1.0        | 1.20        | 0.365   | Good (2.7) | Best pre-fit, smallest gap — opening had no effect |
| KGZ     | 2016     | -9.6        | 2.09        | 0.066*  | Moderate (8.7) | Only marginally significant case; 2022-24 crisis drives it |

## Key Findings

### 1. Only one country approaches significance
- KGZ (p=0.066) is marginally significant, but driven by 2022-2024 crisis
  (gold mine disruption, not demographics)
- MNG (p=0.102) is suggestive but not significant at 10%
- All others p > 0.17

### 2. Pre-fit quality varies enormously
- Best: GEO (RMSPE=2.7) and BLR (2.8) — synthetic matches well
- Worst: RUS (17.9) and TJK (16.6) — synthetic is a poor counterfactual
- RUS is impossible to match because no donor combination replicates
  a large commodity exporter with 1990s collapse dynamics
- When pre-fit is good (GEO, BLR), the post-treatment gap is small → no effect

### 3. Direction of gaps is informative
- MNG, BLR, GEO, KGZ: CA WORSENED after opening (negative gaps)
- RUS, AZE, TJK: CA IMPROVED after opening (positive gaps)
- The improvers are all commodity exporters or conflict-recovery economies
- Pattern: opening → CA deterioration for non-commodity CCA (remittance-dependent)
  but improvement for commodity CCA (export revenue)

### 4. Donor compositions make economic sense
- GEO matched to MNE (43%), AUS, SRB, ESP — small open economies with similar deficits
- BLR matched to HUN (37%), GRC (30%) — transition + EU periphery economies
- MNG matched to YEM (74%) — both remittance-dependent with volatile CA

### 5. Georgia is the cleanest case — and shows no effect
- Best pre-fit (RMSPE=2.7), clear political event (Rose Revolution)
- Average post-treatment gap: -1.0pp (essentially zero)
- p=0.37 (insignificant)
- Interpretation: opening Georgia's capital account had NO detectableeffect on its current account

## Implications for the Paper

1. **SCM produces no strong evidence** that capital account opening causes CA changes
   in CCA countries. The cleanest case (Georgia) shows essentially zero effect.

2. **The approach is limited by small samples and short pre-treatment windows**.
   Most CCA countries have only 4-8 years of pre-treatment data (1992 to opening).
   This makes matching difficult and reduces statistical power.

3. **Commodity confounding** (AZE, RUS) makes SCM unreliable for those cases.
   The oil boom is the dominant post-treatment shock, not opening.

4. **The direction pattern** (non-commodity CCA → CA worsens, commodity → improves)
   is consistent with the broader story: opening allows capital outflows and
   consumption imports in poor countries, while commodity revenues drive surpluses.

5. **For the paper**: report GEO (cleanest, null result), BLR (good fit, modest
   negative), MNG (suggestive negative), and note that commodity cases are
   uninformative. The overall conclusion reinforces Phase 3: opening does not
   produce a clear causal effect on CA.
